Let’s define some vectors which can be used for demonstrations:
manyNumbers <- sample( 1:1000, 20 )
manyNumbers
[1] 298 43 335 69 539 611 6 579 71 260 51 749 662 737 104 910 969 897 218 222
manyNumbersWithNA <- sample( c( NA, NA, NA, manyNumbers ) )
manyNumbersWithNA
[1] 298 222 260 218 897 662 749 NA NA 910 335 NA 737 969 51 539 69 104 43 6 71 579 611
duplicatedNumbers <- sample( 1:5, 10, replace = TRUE )
duplicatedNumbers
[1] 2 3 5 3 1 2 4 4 2 5
letters
[1] "a" "b" "c" "d" "e" "f" "g" "h" "i" "j" "k" "l" "m" "n" "o" "p" "q" "r" "s" "t" "u" "v" "w" "x" "y"
[26] "z"
LETTERS
[1] "A" "B" "C" "D" "E" "F" "G" "H" "I" "J" "K" "L" "M" "N" "O" "P" "Q" "R" "S" "T" "U" "V" "W" "X" "Y"
[26] "Z"
mixedLetters <- c( sample( letters, 5 ), sample( LETTERS, 5 ) )
mixedLetters
[1] "r" "h" "z" "j" "o" "W" "E" "M" "D" "C"
manyNumbersWithNA instead of manyNumbers.all( manyNumbers <= 1000 )
[1] TRUE
all( manyNumbers <= 500 )
[1] FALSE
any( manyNumbers > 1000 )
[1] FALSE
any( manyNumbers > 500 )
[1] TRUE
all( !is.na( manyNumbers ) )
[1] TRUE
any( is.na( manyNumbers ) )
[1] FALSE
Input: logical vector Output: vector of numbers (positions)
which( manyNumbers > 900 )
[1] 16 17
which( manyNumbersWithNA > 900 )
[1] 10 14
which( is.na( manyNumbersWithNA ) )
[1] 8 9 12
manyNumbers[ manyNumbers > 900 ] # indexing by logical vector
[1] 910 969
manyNumbers[ which( manyNumbers > 900 ) ] # indexing by positions
[1] 910 969
somePositions <- which( manyNumbers > 900 )
manyNumbers[ somePositions ]
[1] 910 969
"A" %in% LETTERS
[1] TRUE
c( "X", "Y", "Z" ) %in% LETTERS
[1] TRUE TRUE TRUE
all( c( "X", "Y", "Z" ) %in% LETTERS )
[1] TRUE
all( mixedLetters %in% LETTERS )
[1] FALSE
any( mixedLetters %in% LETTERS )
[1] TRUE
mixedLetters[ mixedLetters %in% LETTERS ]
[1] "W" "E" "M" "D" "C"
mixedLetters[ !( mixedLetters %in% LETTERS ) ]
[1] "r" "h" "z" "j" "o"
manyNumbers %in% 300:600
[1] FALSE FALSE TRUE FALSE TRUE FALSE FALSE TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
[18] FALSE FALSE FALSE
which( manyNumbers %in% 300:600 )
[1] 3 5 8
sum( manyNumbers %in% 300:600 )
[1] 3
NAsif_else( manyNumbersWithNA >= 500, "large", "small" )
[1] "small" "small" "small" "small" "large" "large" "large" NA NA "large" "small" NA
[13] "large" "large" "small" "large" "small" "small" "small" "small" "small" "large" "large"
if_else( manyNumbersWithNA >= 500, "large", "small", "UNKNOWN" )
[1] "small" "small" "small" "small" "large" "large" "large" "UNKNOWN" "UNKNOWN" "large"
[11] "small" "UNKNOWN" "large" "large" "small" "large" "small" "small" "small" "small"
[21] "small" "large" "large"
# here integer 0L is needed instead of real 0.0
# manyNumbersWithNA contains integer numbers and the method complains
if_else( manyNumbersWithNA >= 500, manyNumbersWithNA, 0L )
[1] 0 0 0 0 897 662 749 NA NA 910 0 NA 737 969 0 539 0 0 0 0 0 579 611
unique( duplicatedNumbers )
[1] 2 3 5 1 4
unique( c( NA, duplicatedNumbers, NA ) )
[1] NA 2 3 5 1 4
duplicated( duplicatedNumbers )
[1] FALSE FALSE FALSE TRUE FALSE TRUE FALSE TRUE TRUE TRUE
which.max( manyNumbersWithNA )
[1] 14
manyNumbersWithNA[ which.max( manyNumbersWithNA ) ]
[1] 969
which.min( manyNumbersWithNA )
[1] 20
manyNumbersWithNA[ which.min( manyNumbersWithNA ) ]
[1] 6
range( manyNumbersWithNA, na.rm = TRUE )
[1] 6 969
manyNumbersWithNA
[1] 298 222 260 218 897 662 749 NA NA 910 335 NA 737 969 51 539 69 104 43 6 71 579 611
sort( manyNumbersWithNA )
[1] 6 43 51 69 71 104 218 222 260 298 335 539 579 611 662 737 749 897 910 969
sort( manyNumbersWithNA, na.last = TRUE )
[1] 6 43 51 69 71 104 218 222 260 298 335 539 579 611 662 737 749 897 910 969 NA NA NA
sort( manyNumbersWithNA, na.last = TRUE, decreasing = TRUE )
[1] 969 910 897 749 737 662 611 579 539 335 298 260 222 218 104 71 69 51 43 6 NA NA NA
manyNumbersWithNA[1:5]
[1] 298 222 260 218 897
order( manyNumbersWithNA[1:5] )
[1] 4 2 3 1 5
rank( manyNumbersWithNA[1:5] )
[1] 4 2 3 1 5
sort( mixedLetters )
[1] "C" "D" "E" "h" "j" "M" "o" "r" "W" "z"
manyDuplicates <- sample( 10:15, 10, replace = TRUE )
rank( manyDuplicates )
[1] 7.0 4.5 4.5 1.0 9.5 2.5 2.5 7.0 9.5 7.0
rank( manyDuplicates, ties.method = "min" )
[1] 6 4 4 1 9 2 2 6 9 6
rank( manyDuplicates, ties.method = "random" )
[1] 7 5 4 1 10 2 3 8 9 6
v <- c( -1, -0.5, 0, 0.5, 1, rnorm( 10 ) )
v
[1] -1.0000000 -0.5000000 0.0000000 0.5000000 1.0000000 -0.7318272 0.1974809 -0.2113121 -0.2351181
[10] -0.8045286 -1.7357114 -1.7574160 1.6503601 1.6069962 -0.8519964
round( v, 0 )
[1] -1 0 0 0 1 -1 0 0 0 -1 -2 -2 2 2 -1
round( v, 1 )
[1] -1.0 -0.5 0.0 0.5 1.0 -0.7 0.2 -0.2 -0.2 -0.8 -1.7 -1.8 1.7 1.6 -0.9
round( v, 2 )
[1] -1.00 -0.50 0.00 0.50 1.00 -0.73 0.20 -0.21 -0.24 -0.80 -1.74 -1.76 1.65 1.61 -0.85
floor( v )
[1] -1 -1 0 0 1 -1 0 -1 -1 -1 -2 -2 1 1 -1
ceiling( v )
[1] -1 0 0 1 1 0 1 0 0 0 -1 -1 2 2 0
heights <- c( Amy = 166, Eve = 170, Bob = 177 )
heights
Amy Eve Bob
166 170 177
names( heights )
[1] "Amy" "Eve" "Bob"
names( heights ) <- c( "AMY", "EVE", "BOB" )
heights
AMY EVE BOB
166 170 177
heights[[ "EVE" ]]
[1] 170
expand_grid( x = c( 1:3, NA ), y = c( "a", "b" ) )
# A tibble: 8 x 2
x y
<int> <chr>
1 1 a
2 1 b
3 2 a
4 2 b
5 3 a
6 3 b
7 NA a
8 NA b
combn( c( "a", "b", "c", "d", "e" ), m = 2, simplify = TRUE )
[,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10]
[1,] "a" "a" "a" "a" "b" "b" "b" "c" "c" "d"
[2,] "b" "c" "d" "e" "c" "d" "e" "d" "e" "e"
combn( c( "a", "b", "c", "d", "e" ), m = 3, simplify = TRUE )
[,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10]
[1,] "a" "a" "a" "a" "a" "a" "b" "b" "b" "c"
[2,] "b" "b" "b" "c" "c" "d" "c" "c" "d" "d"
[3,] "c" "d" "e" "d" "e" "e" "d" "e" "e" "e"
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